Lactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing
This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequentia...
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creator | Silva, Emmanuelle P E Moraes, Edgar P Anaya, Katya Silva, Yhelda M O Lopes, Heloysa A P Andrade Neto, Júlio C Oliveira, Juliana P F Oliveira, Josenalde B Rangel, Adriano H N |
description | This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R2 = 0.96 and R2 = 0.98 by MLR and SO, respectively) and of somatic cell count (R2 = 0.97 and R2 = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland. |
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In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R2 = 0.96 and R2 = 0.98 by MLR and SO, respectively) and of somatic cell count (R2 = 0.97 and R2 = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0263714</identifier><identifier>PMID: 35176036</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animals ; Artificial intelligence ; Biology and Life Sciences ; Cattle ; Color ; Computer and Information Sciences ; Cost analysis ; Cow's milk ; Dairy cattle ; Data analysis ; Datasets ; Diagnosis ; Engineering and Technology ; Enzymatic activity ; Enzymes ; Evaluation ; Female ; Frequency analysis ; Health aspects ; Image processing ; Image Processing, Computer-Assisted - methods ; Laboratories ; Lactoperoxidase - analysis ; Lactoperoxidase - metabolism ; Mammary gland ; Mammary glands ; Mastitis ; Mastitis, Bovine - diagnosis ; Mastitis, Bovine - enzymology ; Medical diagnosis ; Medicine and Health Sciences ; Methods ; Milk ; Milk - chemistry ; Multivariate analysis ; Pathogens ; Physical Sciences ; Principal components analysis ; Quality control ; Reagents ; Research and Analysis Methods ; Smartphones ; Statistical analysis ; Statistical methods ; Universal Serial Bus</subject><ispartof>PloS one, 2022-02, Vol.17 (2), p.e0263714-e0263714</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Silva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Silva et al 2022 Silva et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6130a907fc80c417e8124a5144cf4fa1860099dcb4f32aea6abd87dce2a0d5b83</citedby><cites>FETCH-LOGICAL-c692t-6130a907fc80c417e8124a5144cf4fa1860099dcb4f32aea6abd87dce2a0d5b83</cites><orcidid>0000-0001-8297-9678 ; 0000-0001-9005-5272</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853571/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853571/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35176036$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Germon, Pierre</contributor><creatorcontrib>Silva, Emmanuelle P E</creatorcontrib><creatorcontrib>Moraes, Edgar P</creatorcontrib><creatorcontrib>Anaya, Katya</creatorcontrib><creatorcontrib>Silva, Yhelda M O</creatorcontrib><creatorcontrib>Lopes, Heloysa A P</creatorcontrib><creatorcontrib>Andrade Neto, Júlio C</creatorcontrib><creatorcontrib>Oliveira, Juliana P F</creatorcontrib><creatorcontrib>Oliveira, Josenalde B</creatorcontrib><creatorcontrib>Rangel, Adriano H N</creatorcontrib><title>Lactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. 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Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.</description><subject>Animals</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Cattle</subject><subject>Color</subject><subject>Computer and Information Sciences</subject><subject>Cost analysis</subject><subject>Cow's milk</subject><subject>Dairy cattle</subject><subject>Data analysis</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Engineering and Technology</subject><subject>Enzymatic activity</subject><subject>Enzymes</subject><subject>Evaluation</subject><subject>Female</subject><subject>Frequency analysis</subject><subject>Health aspects</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - 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potential in diagnosing subclinical mastitis in cows via image processing</title><author>Silva, Emmanuelle P E ; Moraes, Edgar P ; Anaya, Katya ; Silva, Yhelda M O ; Lopes, Heloysa A P ; Andrade Neto, Júlio C ; Oliveira, Juliana P F ; Oliveira, Josenalde B ; Rangel, Adriano H N</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-6130a907fc80c417e8124a5144cf4fa1860099dcb4f32aea6abd87dce2a0d5b83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Animals</topic><topic>Artificial intelligence</topic><topic>Biology and Life Sciences</topic><topic>Cattle</topic><topic>Color</topic><topic>Computer and Information Sciences</topic><topic>Cost analysis</topic><topic>Cow's milk</topic><topic>Dairy cattle</topic><topic>Data analysis</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Engineering and Technology</topic><topic>Enzymatic 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One</addtitle><date>2022-02-17</date><risdate>2022</risdate><volume>17</volume><issue>2</issue><spage>e0263714</spage><epage>e0263714</epage><pages>e0263714-e0263714</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R2 = 0.96 and R2 = 0.98 by MLR and SO, respectively) and of somatic cell count (R2 = 0.97 and R2 = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35176036</pmid><doi>10.1371/journal.pone.0263714</doi><tpages>e0263714</tpages><orcidid>https://orcid.org/0000-0001-8297-9678</orcidid><orcidid>https://orcid.org/0000-0001-9005-5272</orcidid><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
subjects | Animals Artificial intelligence Biology and Life Sciences Cattle Color Computer and Information Sciences Cost analysis Cow's milk Dairy cattle Data analysis Datasets Diagnosis Engineering and Technology Enzymatic activity Enzymes Evaluation Female Frequency analysis Health aspects Image processing Image Processing, Computer-Assisted - methods Laboratories Lactoperoxidase - analysis Lactoperoxidase - metabolism Mammary gland Mammary glands Mastitis Mastitis, Bovine - diagnosis Mastitis, Bovine - enzymology Medical diagnosis Medicine and Health Sciences Methods Milk Milk - chemistry Multivariate analysis Pathogens Physical Sciences Principal components analysis Quality control Reagents Research and Analysis Methods Smartphones Statistical analysis Statistical methods Universal Serial Bus |
title | Lactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing |
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